Staff Working Paper ERSD-2012-18 Date: 30.10.2012

World Trade Organization
Economic Research and Statistics Division
Testing the Trade Credit and Trade Link:
Evidence from Data on Export Credit Insurance
Marc Auboin Martina Engemann
World Trade Organization University of Munich Manuscript date: October 2012

Abstract

Trade finance has received special attention during the financial crisis as one of the
potential culprits for the great trade collapse. Several researchers have used micro level data
to establish the link between trade finance and trade, especially so during the financial crisis,
and have found diverting results. This paper analyses the effect of trade credit on trade on a
macro level through a whole cycle. We employ Berne Union data on export credit insurance,
the most extensive dataset on trade credits available at the moment, for the period of 2005-
2011. Using an instrumentation strategy we can identify a significantly positive effect of
insured trade credit, as a proxy for trade credits, on trade. The effect of insured trade credit on
trade is very strong and remains stable over the cycle, not varying between crisis and non-
crisis periods. Keywords: trade credit, financial crisis, import estimation.

JEL Classifications: F13, F34, G21, G23

1
Corresponding author: Economic Research and Statistics Division, World Trade Organization, Rue
de Lausanne 154, CH-1211 Geneva 21, Switzerland,
2
Munich Graduate School, University of Munich, Akademiestrasse 1, 80799 München, Germany,
2
I. INTRODUCTION
Interest from academia in the role of trade finance has grown in the context of the

Also the link between financial sector conditions, availability of trade credits and trade needs
to be established over a full cycle.
4
This paper attempts to do so, using for the first time a
database on trade credits large enough to relate it to global trade flows, and a consistent
approach linking finance, trade credits and trade at a macro level.

We have used the largest and most consistent database currently available for trade
finance, that is insured trade credit collected by the members of the Berne Union of export
credit agencies and private export credit insurers, available quarterly per destination country
(almost 100 countries) covering the 2005-2011 period. In addition to the richness of the
database, it is important for the significance of macroeconomic analysis that the total amount
of trade credit recorded annually by the data (close to $1 trillion) be somewhat proportionate
to trade flows ($18 trillion annually for global trade) and overall credit in the countries tested.

3
Eaton et al. (2011) find that demand shocks can explain 80% of the decline in trade and for some
countries, like China and Japan, this share is a lot smaller. Hence, a significant share of the trade collapse
remains to be explained.
4
Note that we use the term trade credit for credit extended to finance international transactions (not for
domestic transactions).

3
This enables us to make statements about aggregate effects which can complement previous
micro level studies. We have used short-term trade credit data to relate credit to other
quarterly flows such as GDP, trade and money.
5
trade transactions, which our paper therefore tested successfully at the macro level. Testing
this link at the macroeconomic level is important, as some other studies remained
inconclusive, when using a micro approach, about the impact of trade finance on trade, in
particular during the great trade collapse of 2009 (see e.g. Paravisini et al. (2011), Levchenko
et al. (2011) and Behrens et al. (2011)).

Second, our paper confirms some of the findings by earlier studies using trade credit
insurance data, albeit on a smaller scale, generally data provided by individual export credit
insurers (see Van der Veer (2010), Felbermayr and Yalcin (2011), Felbermayr, Heiland, and
Yalcin (2012), Moser et al. (2008) and Egger and Url (2006)). Using data on a single private
credit insurer, Van der Veer (2010) establishes a causal link between exports and the private
supply of credit insurance, also using the insurer's claims ratio as an instrument for insured
exports. Felbermayr and Yalcin (2011) estimate the effect of export credit insurance on

5
80% of total credit insured is short-term, only 20% is long-term (over a year) (IMF-BAFT, 2009).

4
exports using data of the German export credit agency Euler-Hermes applying a fixed effects
estimator, not instrumenting the credit insurance variable. Our dataset includes the data from
more than 70 export credit agencies and private export credit insurers. These insurers account
for more than 90% of the insured trade credit market. Furthermore, as in Van der Veer (2010)
we can establish a causal link between insured trade credit and trade, using the actual risk of
trade credit insurance as an instrument for insured trade credit.

The paper is structured as follows: Section 2 introduces the dataset and gives
summary statistics. Section 3 explains our empirical strategy. Section 4 then presents our
empirical results. Finally, Section 5 gives a conclusion.
7 For the time being, the largest source of regularly collected, methodologically
consistent data on trade finance is data collected by trade credit and investment insurers.

6
For example, under a letter of credit, the bank of the buyer provides a guarantee to the seller that it
will be paid regardless of whether the buyer ultimately fails to pay. The risk that the buyer will fail to pay is
hence transferred from the seller to the letter of credit's issuer.
7
Documents from the G-20 in Cannes (2011) refer to the need to improve statistical information on
trade finance (see report of the Development Working Group).

5
They collect data on trade credit, which is subject to insurance. As any credit, an insurance
against default can be obtained from these insurers.

1. Berne Union Data

Export credit insurers, both public and private, provide insurance on trade credits,
thereby reducing the commercial and political risk for trading partners. Insurance may apply
to bank-intermediated trade credit, i.e., letters of credit and the like, and inter-firm trade
credit, e.g. suppliers and buyers' credit. In the case of inter-firm credit, the export credit
insurer guarantees to indemnify an exporter in case the importer fails to pay for the goods or
services purchased. In return, the export credit insurer charges the exporter a premium. In the
case of bank-intermediated credit, the export credit insurer would relieve the importers' and
the exporters' bank from some of the commercial risk involved in the transaction.

Berne Union data provides data on insured trade credit, hence on an important part of

from US$ 6 to 10 trillion a year. Hence, Berne Union data capture a reasonable share of it –
again, by far the most extensive dataset available at the moment. 6
Additionally, the Berne Union reports data on short-term claims paid by destination
countries which captures the actual risk of the trade credit insurance activity. In the case of an
inter-firm credit, if the buyer fails to pay for the goods purchased, the exporter can apply for
compensation of its loss under the insurance policy. Thus, claims paid measure the amount
which exporters have been indemnified for by their export credit insurance. Claims paid
increase in times in which political and/ or commercial risk rises.

2. Country Characteristics

Our aim is to study the relation between the overall credit market and insured trade
credit, and between insured trade credit and trade. The Berne Union provides for credit
insurance data by destination country, not by country of origin. Hence, we analysed the
impact of insured trade credit on the destination country's aggregate imports. WTO quarterly
data on countries' imports of merchandise and commercial services are used. Real imports
have been obtained by applying deflators from the IMF International Financial Statistics
(IFS).
8 Data on gross domestic product (GDP) is taken from the World Development
Indicators of the World Bank, thus deflated by a common price deflator. For the relative price
measure, the recent dataset on real effective exchange rates produced by the Bruegel Institute
is used (for a detailed description of the dataset, see Bruegel, 2012). The real effective
exchange rate is calculated against a basket of currencies of 138 trading partners. The real
effective exchange rate is calculated as

8
Note that the data does not include public services.

7
3. Summary Statistics on the Relation between Insured Trade Credit and Imports

Our sample comprises 91 countries from the first quarter of 2005 till the fourth
quarter of 2011 (unbalanced panel). Among the 91 countries, 35 are high income countries,
26 are upper-middle income countries, 21 lower-middle income countries and 9 low income
countries according to the World Bank's country classification by income groups.
9
With these
destination countries, we account for about three-quarters of world imports of goods and
services. The list of countries included in our sample can be found in Table 2 in the Appendix.

Trade credit has proved to be important for international trade, and with it trade credit
insurance, during the financial crisis. Figure 1 looks at the relationship between insured trade
credit and imports over the recent economic cycle, by taking the average of all countries. It
shows that both imports and short-term insured trade credits increased until the beginning of
2008. Short-term insured trade credit thus fell quite sharply in the second quarter of 2008,
slightly before imports which collapsed one quarter later, at the end of 2008. In the second
half of 2009, imports have been recovering, reaching their pre-crisis level at the end of 2010.
Figure 1 may at first sight be interpreted as establishing a link between insured trade credit
and the great trade collapse in 2008, the one preceding the other. However, no causal
interpretation can actually be established from this apparent correlation.

Figure 1: The relation between imports and insured trade credits in million US$ (averaged
over all countries)
trillion in short-term trade transactions (ICC 2011). Figure 2: The relation between short-term insured trade credits and short-term claims paid
over time (averaged over all countries)
In Figure 2 the relation between short-term insured trade credits and short-term claims
paid over time is illustrated, albeit the two variables are on different scales. Short-term

9
insured trade credits and short-term claims paid seem to be somewhat negatively correlated
over time.

Short-term claims paid increased during the financial crisis in 2009, and insured trade
credits were reduced. Indeed, the small ratio of claims paid to short-term insured trade credits
indicate that, even in the low part of the cycle, the risk level for such activity has remained
small (for example relative to claim/default on other forms of credit, such as real estate-
related credit, at the same period). A supply effect may explain why the increase in claims led
export credit insurers to reduce somewhat their short-term credit exposure, despite the
absolute low level of risk. When credit insurers observe rising claims, i.e. higher actual risks,
they might adjust the risk profile and the amounts they commit to insure according to changes
in country and company risk.

However, a comparison between gross insured trade credits and gross claims might be
somewhat misleading. Countries importing the most generally have higher volumes of
insured trade credit and consequently more claims paid. Hence, using total gross short-term
claims paid as a total measure of risk may not be appropriate. Instead, we have used the share
of claims paid out of total credit insured for a country as our preferred risk measure.




+



+



+



+

+

(1)



=

+





the fourth quarter 2009 and 0 otherwise.
10


is a liquidity measure for which we use the
monetary aggregate M1 of country j in quarter t-2. 

measures absolute real GDP of
country j in quarter t-1. 

is a measure of country j’s relative price of foreign and
domestic goods in quarter t-1, where we use the real effective exchange rate. 

are
aggregate imports of country j in quarter t. Finally, 

and 

are country fixed effects and 


and 

 are the idiosyncratic errors.

In equation (1) short-term insured trade credit is regressed on its measure of risk (the
share of short-term claims paid), on the level of liquidity in the economy linked to real
transactions (M1), on a measure of relative prices between countries (real effective exchange
rates), on real GDP, and on a crisis dummy. Taking these explanatory variables individually,
we presume the share of claims paid to have a negative effect, and M1 as a measure of

economic activity, relative prices, and the crisis dummy as explanatory variables for insured
trade credits, they are also needed in the first stage equation from a technical point of view, as
they are exogenous explanatory variables of the second-stage. 10
One may argue that the financial crisis already started earlier. However, the real crisis began with the
crash of Lehman Brothers in the third quarter of 2008.

11
Equation (2) incorporates insured trade credit as a determinant of the standard,
macroeconomic equation for imports, imports depending normally on national income, and
on relative prices of foreign and domestic goods (see for example, Goldstein and Khan, 1985,
and Emran and Shilpi, 2010, on import demand estimation).
11
We regress a country's
aggregate real imports in quarter t on the predicted value of short-term insured trade credits
obtained from the first-stage equation, the standard controls of import equations, real GDP
and the real effective exchange rate, and the crisis dummy. As it is well established, real GDP,
as a measure of the size of an economy, should have a positive impact on real imports.
Following the same reasoning as above, the real effective exchange rate may have a negative
effect on imports in the short-run, i.e., in the time span of the estimation period. This effect
would normally turn positive if we considered much longer lags (J-curve effects are thought
to last between six and twelve months, perhaps more, see Krugman and Obstfeld, 2009), but
this is not the case in this study. Under Equation (2), we also presume the financial crisis
dummy to have a negative impact on imports, as trade collapsed during the financial crisis.
Not including these variables as additional controls to the insured trade credit variable would
lead to an omitted variables bias as they would be included in the error term of the estimation
equation.


the risk of non-payment of the trading partner, 

, and the total turnover of insured trade
credit over the period. In order to only control for the risk of non-payment, which influences

11
We do not use the standard gravity equation as we think it is less suited for addressing the
endogeneity concerns we have regarding insured trade credits. Furthermore, we do not have bilateral trade credit
data but data on short-term insured trade credits by destination countries only. Therefore, we rely with our
specification on the classical import estimation equation adding trade finance as an explanatory variable.

12
short-term insured trade credits but reversely is not influenced by short-term insured trade
credits, we thus have to divide claims paid by the total turnover:





=

.

The instrument is valid as it does have a significantly negative impact on short-term
insured trade credits and does not influence imports directly but only via its effect on insured
trade credits. In addition, we use liquidity as a second instrument as it influences trade credits
but does not have a direct influence on imports. Hence, the instruments are relevant. In order
to check for the strength of the instruments, we report the F-statistics in the first-stage
regression of Table 1a. The F-statistics, except of one, are well above 10, the threshold
recommended by Staiger and Stock (1997) commonly referred to in the literature. As we


Tables 1a and 1b contain the first-stage and second-stage results of our main
specification. Columns 1 to 3 of these tables give the two-stage-least-squares (2SLS), random
effects instrumental variable (RE IV) and the fixed effects instrumental variable estimator
(FE IV) results, respectively, with the beta coefficients reported next to it. 13
Table 1a and 1b: First-stage and second-stage results of the import estimation (1) (2) (3)
VARIABLES L.lSTtrade
credit
Beta
coefficients
L.lSTtrade
credit
Beta
coefficients
L.lSTtrade
credit
Beta
coefficients

L.lrealgdp 0.739***
0.687
1.133***
1.053
1.424***

R-squared 0.887 0.859 0.855
Number of countries 91 91 91
F statistic 748.85 15.77 6.51

Test for over identification



0.0129 0.0720 0.0142
p-value 0.909 0.788 0.905 (1) (2) (3)
VARIABLES lrealimports Beta
coefficients
lrealimports Beta
coefficients
lrealimports Beta
coefficients

L.lSTtrade credit 0.412***
0.487
0.365***
0.432
0.322**
0.381
(0.0206) (0.124) (0.140)
L.lrealgdp 0.470***
0.516
0.459***

well as the overall level of real economic activity (as measured by real GDP) have strong
explanatory effects on insured trade credit supplied at any point in time.

With respect to risk and money, one would expect the former to have a negative effect
on insured trade credit, and the latter to have a positive effect. Both came out clearly in the
regression. The risk of credit insurance, measured as the share of claims per total turnover of
insured trade credit, has a significant negative impact on insured trade credits.
12
This can be
explained via the supply side, credit insurers being more hesitant to extend credit insurance
during risky times. However, we see that this effect, while significant, is relatively small.
That can be explained by the fact that, while being more prudent in choosing new exposures,
credit insurers tend to support their customers during periods of increased risk. The liquidity
measure M1 has a significantly positive effect on insured trade credits, which seems to be
mainly driven by differences in liquidity between countries. This confirms that the overall
conditions of liquidity in the economy have a sizable impact on the availability of trade
credits, through insured trade credits.

With respect to real economic activity, it also appears that real GDP has a significant
positive effect on short-term insured trade credits. The coefficients imply that a 1% increase
in real GDP leads to a 0.7 to 1.4% increase in short-term insured trade credits. Controlling for
observed and unobserved country fixed effects leads to an increase in the real GDP
coefficient. Hence, there seems to be roughly a 1-to-1 relation between a change in GDP and
the change in insured trade credits. Larger countries have a higher demand for insured trade
credits, which should lead to a less than proportional effect of real GDP on trade credit
because only part of the production is traded. At the same time, export credit insurers are
probably also more willing to extend insurance to firms exporting to larger economies, which
explain the proportional effect of GDP on trade credit.

The crisis dummy is insignificant in the 2SLS estimation, not considering the panel

Under these coefficients, real GDP comes up by far as the variable with the strongest
explanatory power. Overall, an 

of about 0.85 seems to suggest that our explanatory
variables of the first-stage equation have sufficient explanatory power.

Causal effect of trade credits on imports (Table 1b)

Independently of the specification, short-term, insured trade credits have a positively
significant effect on real imports (Table 1b). For an increase by 1% of insured trade credits in
country j, country j's imports increase by 0.4 %. This means in effect that the 27.8% drop of
insured trade credit from its peak value of over US$ 1 trillion in the second quarter of 2008 to
US$ 734 billion in the first quarter of 2010, would be responsible for a reduction in real
imports by about 11 % (hence, in a total of 7 quarters). Therefore, one can confirm the
findings by Amiti and Weinstein (2011), and Chor and Manova (2012), whereby trade
finance gaps have a significant impact on trade flows, at a macro level.

Additionally, real GDP has a statistically significant impact on real imports. A 1 %
increase in real GDP, which can be seen as a measure of overall demand/national income,
leads in this specification to a 0.5 % increase in real imports. The income elasticity would be
larger if we did not control for insured trade credit (see Houthakker and Magee (1969) and
Marquez (2002) for a discussion of income elasticities of import equations). Though, it is in
line with the finding of Senhadji (1998) that imports react relatively slowly to changes in
domestic income. His results show that short-run income elasticities are on average less than
0.5, whereas long-run income elasticities are close to 1.5.

Table 1b results also show that the crisis dummy has a significantly negative effect,
which could be anticipated, as imports literally collapsed during the crisis.

The real effective exchange rate is significant in the 2SLS estimation but insignificant

due to the assumption that 

and 

are uncorrelated with 

and 

, it should be our
preferred estimation as the coefficients do not systematically differ from the ones of the FE
IV estimator and RE IV is more efficient than FE IV. 2. Robustness Checks

Testing for heterogeneous effects of trade credits

Since the financial crisis has played an important role in drawing the attention to the
role of trade credits on trade, we tested whether the trade credit effect differed during crisis
and non-crisis periods in Table 4. To do so, we included in the specification a term
(L.lSTtrade credit*Crisis) allowing for the interaction between the crisis dummy and short-
term trade credit - the interaction term, measuring the specific effect of trade credit on real
imports during the period of crisis. The coefficient of the trade credit variable (L.lSTtrade
credit) measures the effect of trade credit on imports during the non-crisis period.

During the non-crisis period, from 2005 to 2008, and 2010 to 2011, the trade credit
elasticity of real imports lies between 0.3 and 0.4. The interaction term for the crisis period,
by being insignificant, means that there is no difference in the trade credit effect during the
crisis and non-crisis periods, hence this effect remains stable over the whole cycle/period.
This seems surprising, as we had thought that the effect of trade credit could have been much

maximisation (EM) algorithm. Imputation does not generate the true values of claims paid
but enables us to handle the data in a way that leads to valid statistical inference. Therefore,
w
e generate 120 plausible values for short-term claims paid (see Heitjan and Rubin (1990) for
a discussion on how many imputed datasets one should generate). Thus, we recalculate
coefficients and standard errors taking into account that values are imputed (see Rubin, 1987).

Table 5 presents second-stage results using the multiply imputed short-term claims
data. The effect of short-term insured trade credits on imports remains significantly positive.
It even increases from 0.4 to about 0.5. Likewise, real GDP remains significantly positive,
though it is insignificant in the FE IV regression and for all estimations the size of the effect
decreases. The crisis dummy remains significantly negative and very stable. As before, the
real effective exchange rate comes out as significant in the 2SLS regression, but it is not in
the RE and FE IV regressions.

Overall, the above checks applied to our estimation results comfort the generally
strong robustness of these results. V. CONCLUSION This paper establishes a strong causal link between short-term trade credit insurance,
as a measure of trade credit, and trade at a macro level through a full cycle. Using quarterly
country-level data of export credit insurers from the Berne Union for the period of 2005 to
2011, we find that a 1 % increase in trade credit granted to a country leads to a 0.4 % increase
in real imports of that country. This effect does not vary between crisis and non-crisis periods.
These results stress the importance of trade finance for international trade. Although the
debate on the great trade collapse shed the light on the role of trade credit during periods of
crises, trade credit appears to be equally important in non-crisis periods. The policy lesson to


19
BIBLIOGRAPHY Amiti, Mary and David E. Weinstein (2011), "Exports and Financial Shocks", The Quarterly
Journal of Economics (2011), Vol. 126 (4), pp. 1841-1877.

Antràs, Pol and Fritz Foley (2011), "Poultry in Motion: A Study of International Trade
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Baldwin, Richard (2009), " The Great Trade Collapse: Causes, Consequences and Prospects",
VoxEU.org ebook.

Behrens, Kristian, Gregory Corcos, and Giordano Mion (2011), "Trade Crisis? What Trade
Crisis?", CEPR Discussion Paper no. 7956.

Berne Union (2010), "Credit insurance in support of international trade: Observations
throughout the crisis".

Bricongne, Jean-Charles, Lionel Fontagné, Guillaume Gaulier, Daria Taglioni, and Vincent
Vicard (2012), "Firms and the global crisis: French exports in the turmoil", Journal of
International Economics, Vol. 87, pp. 134-146.

Bruegel (2012), "Real Effective Exchange Rates for 178 Countries: A new Database",
Bruegel Working Paper 2012/06.

Chor, Davin and Kalina Manova (2009), “Off the Cliff and Back? Credit Conditions and
International Trade during the Global Financial Crisis”, Stanford University mimeo.


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Hausman, Jerry A. (1983), "Specification and Estimation of Simultaneous Equation Models",
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".

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22
APPENDIX Table 2: List of countries included in the estimation sample

___________________________________________________________________________

Albania
Algeria
Argentina
Armenia
Australia
Austria
Bahamas, The
Bangladesh
Belarus
Belgium
Belize
Bolivia
Bosnia and Herzegovina
Botswana
Brazil
Bulgaria
Cambodia
Cape Verde
Chile
China, P.R.: Hong Kong
China, P.R.: Mainland
Colombia

Lithuania
Luxembourg
Macedonia, FYR
Malaysia
Malta
Mexico
Moldova
Mongolia
Morocco
Mozambique
Namibia
Nepal
Netherlands
New Zealand
Pakistan
Paraguay
Poland
Portugal
Qatar
Romania
Samoa
Saudi Arabia
Seychelles
Singapore
Slovak Republic
Slovenia
South Africa
Spain
Sri Lanka
Sudan


STtrade credit 7,352.37 12,825.22 1.1 73,254.7 1,776

Log(STtrade credit) 7.21 2.29 0.1 11.2 1,776

Real imports 36,312.07 76,431.91 29.9 613,943.5 1,776

Log(Real imports) 8.98 1.94 3.39 13.33 1,776

Real GDP 123,913.2 387,834.2 64.1 3,345,458 1,776

Log(Real GDP) 9.76 2.13 4.16 15.02 1,776

M1 1,096,748 2,195,367 27.4 6,994,741 1,776

Log(M1) 10.29 3.21 3.31 15.76 1,776

ST claims paid 2.83 7.01 0 119.4 1,776

Log(ST claims paid) -4.07 6.58 -13.82 4.78 1,776

ST claims per credit 0.97 2.44 0 42.5 1,776

Log(ST claims per credit) -4.87 6.11 -13.82 3.75 1,776

Real effective exchange
rate (reer)
100.57 9.74 51.7 159.2 1,776

Log(reer) 4.61 0.09 3.95 5.07 1,776

0.267*
0.318
(0.0212) (0.146) (0.158)
L.lSTtrade credit*Crisis 0.0121 0.022 -0.0019 -0.004 -0.0026 -0.005
(0.0129) (0.0042) (0.0043)
L.lrealgdp 0.470***
0.516
0.492***
0.536
0.483**
0.527
(0.0211) (0.184) (0.227)
Crisis -0.218***
-0.052
-0.130***
-0.031
-0.123***
-0.029
(0.0732) (0.0292) (0.0301)
L.lreer -0.296***
-0.015
-0.0486 -0.003 -0.0056 -0.0002
(0.0968) (0.0631) (0.0901)
Constant 2.856*** 2.269*** 2.415***
(0.451) (0.565) (0.731)

Estimation Method 2SLS RE IV FE IV
Observations 1,776 1,776 1,776
R-squared 0.957 0.959 0.959
Number of countries 91 91 91

-0.169***
-0.041
-0.167***
-0.040
(0.0293) (0.0167) (0.0195)
L.lreer -0.581***
-0.029
-0.089 -0.004 0.0188 0.0009
(0.1174) (0.059) (0.106)
Constant 3.129*** 1.353* 1.737
(0.551) (0.770) (1.293)

Estimation Method 2SLS RE IV FE IV
Observations 1,776 1,776 1,776
Number of countries 91 91 91
Number of imputations 120 120 120

Robust standard errors in parentheses, applying Rubin's adjustment of standard errors for multiple
imputations (Rubin, 1987). *** p<0.01, ** p<0.05, * p<0.